The embodiments relate to computer-aided diagnosis (CAD) using images and, more particularly, to a computer-aided diagnosis using a plurality of interpretation results based on different algorithms.
Computer-aided diagnosis is a technique of presenting the position of a focus candidate detected from an input digital image or an quantitative analysis result using the computer on an image in an easily understandable manner by using an image pattern recognition technique in order to reduce the load of image diagnosis on a doctor or technician, assuming that the doctor makes final determination in diagnosis. This technique is used, for example, for lung cancer screening using chest X-ray pictures and CT images for mass screening, automatic breast cancer mammography screening systems, stomach cancer detection systems, circulatory organ Doppler diagnosis in ultrasonic image diagnosis, and the like.
Recently, automatic diagnosis support apparatuses using such computer-aided diagnosis have used a technique called the MT (Mahalanobis-Taguchi) system. The MT system is a technique of forming a new criterion by synthesizing polyvariant items (multivariates) in quality engineering. The types of such technique include the MT (Mahalanobis Taguchi) method, T (Taguchi) method, MTA (Mahalanobis Taguchi Ajoint) method, and TS (Taguchi Schmitt) method. About five years have passed since this MT system was begun to be applied to normality/abnormality identification/determination. Recently, a special application of the system has been made to implement identification using factorial effect patterns of SN ratios in discriminating the type of liver disease.
The conventional automatic diagnosis support apparatus, however, has the following problems.
The mainstream image pattern recognition techniques include a neural network method, principal component analysis method, and multivariate analysis method (regression analysis/factor analysis method), and have no consistency in interaction, model pattern extraction, parameter variation range, main component selection scheme, and the like. For this reason, no general-purpose analysis technique has been established yet, and hence the image pattern recognition technique lacks in credibility and reproducibility.
In addition, a liver disease type discrimination method using the MT system analyzes factor patterns which increase the distances of individual cases in abnormal patterns based on the state space of able-bodied persons. For this reason, even if, for example, this method allows suitable automatic determination in the case of the liver, it is not clear that the same method is useful for the discrimination of other diseases (versatile).